



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__A							       =		{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}



def 			__a	(       lowerCAmelCase_  :   Optional[Any]							) ->					Any:






						'''simple docstring'''



						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts							)
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config							)
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights							)

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0



def 			__a	(       lowerCAmelCase_  :   Optional[int]     ,lowerCAmelCase_  :   Optional[Any]							) ->					Dict:






						'''simple docstring'''



						if args.student_type == "roberta":
												UpperCAmelCase_=  False
						elif args.student_type == "gpt2":
												UpperCAmelCase_=  False



def 			__a	(       lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   Dict							) ->					List[Any]:






						'''simple docstring'''



						if args.student_type == "roberta":
												UpperCAmelCase_=  False



def 			__a	(       ) ->					Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=  argparse.ArgumentParser(description="""Training"""							)
						parser.add_argument("""--force"""     ,action="""store_true"""     ,help="""Overwrite dump_path if it already exists."""							)

						parser.add_argument(
						    """--dump_path"""     ,type=A_     ,required=A_     ,help="""The output directory (log, checkpoints, parameters, etc.)"""							)
						parser.add_argument(
						    """--data_file"""     ,type=A_     ,required=A_     ,help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""     ,)

						parser.add_argument(
						    """--student_type"""     ,type=A_     ,choices=["""distilbert""", """roberta""", """gpt2"""]     ,required=A_     ,help="""The student type (DistilBERT, RoBERTa)."""     ,)
						parser.add_argument("""--student_config"""     ,type=A_     ,required=A_     ,help="""Path to the student configuration."""							)
						parser.add_argument(
						    """--student_pretrained_weights"""     ,default=A_     ,type=A_     ,help="""Load student initialization checkpoint."""							)

						parser.add_argument(
						    """--teacher_type"""     ,choices=["""bert""", """roberta""", """gpt2"""]     ,required=A_     ,help="""Teacher type (BERT, RoBERTa)."""							)
						parser.add_argument("""--teacher_name"""     ,type=A_     ,required=A_     ,help="""The teacher model."""							)

						parser.add_argument("""--temperature"""     ,default=2.0     ,type=A_     ,help="""Temperature for the softmax temperature."""							)
						parser.add_argument(
						    """--alpha_ce"""     ,default=0.5     ,type=A_     ,help="""Linear weight for the distillation loss. Must be >=0."""							)
						parser.add_argument(
						    """--alpha_mlm"""     ,default=0.0     ,type=A_     ,help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""     ,)
						parser.add_argument("""--alpha_clm"""     ,default=0.5     ,type=A_     ,help="""Linear weight for the CLM loss. Must be >=0."""							)
						parser.add_argument("""--alpha_mse"""     ,default=0.0     ,type=A_     ,help="""Linear weight of the MSE loss. Must be >=0."""							)
						parser.add_argument(
						    """--alpha_cos"""     ,default=0.0     ,type=A_     ,help="""Linear weight of the cosine embedding loss. Must be >=0."""							)

						parser.add_argument(
						    """--mlm"""     ,action="""store_true"""     ,help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""							)
						parser.add_argument(
						    """--mlm_mask_prop"""     ,default=0.15     ,type=A_     ,help="""Proportion of tokens for which we need to make a prediction."""     ,)
						parser.add_argument("""--word_mask"""     ,default=0.8     ,type=A_     ,help="""Proportion of tokens to mask out."""							)
						parser.add_argument("""--word_keep"""     ,default=0.1     ,type=A_     ,help="""Proportion of tokens to keep."""							)
						parser.add_argument("""--word_rand"""     ,default=0.1     ,type=A_     ,help="""Proportion of tokens to randomly replace."""							)
						parser.add_argument(
						    """--mlm_smoothing"""     ,default=0.7     ,type=A_     ,help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""     ,)
						parser.add_argument("""--token_counts"""     ,type=A_     ,help="""The token counts in the data_file for MLM."""							)

						parser.add_argument(
						    """--restrict_ce_to_mask"""     ,action="""store_true"""     ,help="""If true, compute the distillation loss only the [MLM] prediction distribution."""     ,)
						parser.add_argument(
						    """--freeze_pos_embs"""     ,action="""store_true"""     ,help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""     ,)
						parser.add_argument(
						    """--freeze_token_type_embds"""     ,action="""store_true"""     ,help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""     ,)

						parser.add_argument("""--n_epoch"""     ,type=A_     ,default=3     ,help="""Number of pass on the whole dataset."""							)
						parser.add_argument("""--batch_size"""     ,type=A_     ,default=5     ,help="""Batch size (for each process)."""							)
						parser.add_argument(
						    """--group_by_size"""     ,action="""store_false"""     ,help="""If true, group sequences that have similar length into the same batch. Default is true."""     ,)

						parser.add_argument(
						    """--gradient_accumulation_steps"""     ,type=A_     ,default=50     ,help="""Gradient accumulation for larger training batches."""     ,)
						parser.add_argument("""--warmup_prop"""     ,default=0.05     ,type=A_     ,help="""Linear warmup proportion."""							)
						parser.add_argument("""--weight_decay"""     ,default=0.0     ,type=A_     ,help="""Weight decay if we apply some."""							)
						parser.add_argument("""--learning_rate"""     ,default=5E-4     ,type=A_     ,help="""The initial learning rate for Adam."""							)
						parser.add_argument("""--adam_epsilon"""     ,default=1E-6     ,type=A_     ,help="""Epsilon for Adam optimizer."""							)
						parser.add_argument("""--max_grad_norm"""     ,default=5.0     ,type=A_     ,help="""Max gradient norm."""							)
						parser.add_argument("""--initializer_range"""     ,default=0.02     ,type=A_     ,help="""Random initialization range."""							)

						parser.add_argument(
						    """--fp16"""     ,action="""store_true"""     ,help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""     ,)
						parser.add_argument(
						    """--fp16_opt_level"""     ,type=A_     ,default="""O1"""     ,help=(
						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    )     ,)
						parser.add_argument("""--n_gpu"""     ,type=A_     ,default=1     ,help="""Number of GPUs in the node."""							)
						parser.add_argument("""--local_rank"""     ,type=A_     ,default=-1     ,help="""Distributed training - Local rank"""							)
						parser.add_argument("""--seed"""     ,type=A_     ,default=56     ,help="""Random seed"""							)

						parser.add_argument("""--log_interval"""     ,type=A_     ,default=5_00     ,help="""Tensorboard logging interval."""							)
						parser.add_argument("""--checkpoint_interval"""     ,type=A_     ,default=40_00     ,help="""Checkpoint interval."""							)
						UpperCAmelCase_=  parser.parse_args()
						sanity_checks(A_							)

						# ARGS #
						init_gpu_params(A_							)
						set_seed(A_							)
						if args.is_master:
												if os.path.exists(args.dump_path							):
																		if not args.force:
																								raise ValueError(
																								    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    """ itUse `--force` if you want to overwrite it"""							)
																		else:
																								shutil.rmtree(args.dump_path							)

												if not os.path.exists(args.dump_path							):
																		os.makedirs(args.dump_path							)
												logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""							)

												# SAVE PARAMS #
												logger.info(F"""Param: {args}"""							)
												with open(os.path.join(args.dump_path     ,"""parameters.json"""							)     ,"""w"""							) as f:
																		json.dump(vars(A_							)     ,A_     ,indent=4							)
												git_log(args.dump_path							)

						UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  MODEL_CLASSES[args.student_type]
						UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase_=  teacher_tokenizer_class.from_pretrained(args.teacher_name							)
						UpperCAmelCase_=  {}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase_=  tokenizer.all_special_tokens.index(A_							)
												UpperCAmelCase_=  tokenizer.all_special_ids[idx]
						logger.info(F"""Special tokens {special_tok_ids}"""							)
						UpperCAmelCase_=  special_tok_ids
						UpperCAmelCase_=  tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(F"""Loading data from {args.data_file}"""							)
						with open(args.data_file     ,"""rb"""							) as fp:
												UpperCAmelCase_=  pickle.load(A_							)

						if args.mlm:
												logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""							)
												with open(args.token_counts     ,"""rb"""							) as fp:
																		UpperCAmelCase_=  pickle.load(A_							)

												UpperCAmelCase_=  np.maximum(A_     ,1							) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		UpperCAmelCase_=  0.0  # do not predict special tokens
												UpperCAmelCase_=  torch.from_numpy(A_							)
						else:
												UpperCAmelCase_=  None

						UpperCAmelCase_=  LmSeqsDataset(params=A_     ,data=A_							)
						logger.info("""Data loader created."""							)

						# STUDENT #
						logger.info(F"""Loading student config from {args.student_config}"""							)
						UpperCAmelCase_=  student_config_class.from_pretrained(args.student_config							)
						UpperCAmelCase_=  True

						if args.student_pretrained_weights is not None:
												logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""							)
												UpperCAmelCase_=  student_model_class.from_pretrained(args.student_pretrained_weights     ,config=A_							)
						else:
												UpperCAmelCase_=  student_model_class(A_							)

						if args.n_gpu > 0:
												student.to(F"""cuda:{args.local_rank}"""							)
						logger.info("""Student loaded."""							)

						# TEACHER #
						UpperCAmelCase_=  teacher_model_class.from_pretrained(args.teacher_name     ,output_hidden_states=A_							)
						if args.n_gpu > 0:
												teacher.to(F"""cuda:{args.local_rank}"""							)
						logger.info(F"""Teacher loaded from {args.teacher_name}."""							)

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(A_     ,A_							)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(A_     ,A_							)

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0							) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						UpperCAmelCase_=  Distiller(
						    params=A_     ,dataset=A_     ,token_probs=A_     ,student=A_     ,teacher=A_							)
						distiller.train()
						logger.info("""Let's go get some drinks."""							)


if __name__ == "__main__":
	main()






def a__       (			A_   ):






  '''simple docstring'''





  if len(A_   ) < 2:
    return collection

  def circle_sort_util(A_,	A_,	A_   ) -> bool:
    __magic_name__								=			False

    if low == high:
      return swapped

    __magic_name__								=			low
    __magic_name__								=			high

    while left < right:
      if collection[left] > collection[right]:
        __magic_name__	,       __magic_name__								=			(
            collection[right],
            collection[left],
        )
        __magic_name__								=			True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __magic_name__	,       __magic_name__								=			(
          collection[right + 1],
          collection[left],
      )

      __magic_name__								=			True

    __magic_name__								=			low + int((high - low) / 2   )
    __magic_name__								=			circle_sort_util(A_,	A_,	A_   )
    __magic_name__								=			circle_sort_util(A_,	mid + 1,	A_   )

    return swapped or left_swap or right_swap

  __magic_name__								=			True

  while is_not_sorted is True:
    __magic_name__								=			circle_sort_util(A_,	0,	len(A_   ) - 1   )

  return collection


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	Tuple       			=  [int(item) for item in user_input.split(',')]
    print(circle_sort(unsorted))

